Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “decision analysis”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,009 records · Page 56

An experiment with interactive planning models

Experiments on decision making in planning problems are described. Executives were tested in dealing with capital investments and competitive pricing decisions under conditions of uncertainty. A software package, the interactive risk analysis model system, was developed, and two controlled experiments were conducted. It is concluded that planning models can aid management, and predicted uses of the models are as a central tool, as an educational tool, to improve consistency in decision making, to improve communications, and as a tool for consensus decision making.

Beville, J.↗

Space station software reliability analysis based on failures observed during testing at the multisystem integration facility

Quality of software not only is vital to the successful operation of the space station, it is also an important factor in establishing testing requirements, time needed for software verification and integration as well as launching schedules for the space station. Defense of management decisions can be greatly strengthened by combining engineering judgments with statistical analysis. Unlike hardware, software has the characteristics of no wearout and costly redundancies, thus making traditional statistical analysis not suitable in evaluating reliability of software. A statistical model was developed to provide a representation of the number as well as types of failures occur during software testing and verification. From this model, quantitative measure of software reliability based on failure history during testing are derived. Criteria to terminate testing based on reliability objectives and methods to estimate the expected number of fixings required are also presented.

Tamayo, Tak Chai↗

RISMC Analysis Framework in Response to Proposed 10 CFR 50.46c Rulemaking

The results obtained from the demonstration calculations indicate that as an automation tool in LOCA analyses, LOTUS has the capability to provide integrated evaluation through seamless interfaces of the data stream in multiple disciplines. With further more development, LOTUS will provide a systematic way to perform uncertainty quantification, sensitivity analysis and risk assessment of the multiple physics involved in the LOCA analysis in response to the proposed 10 CFR 50.46c rulemaking. It can be used to obtain quantitative estimate of design or operational margin loss or gain as well as to assess the impact on LOCA analysis safety metrics associated with various combinations of changes in LOCA analysis inputs. It can also aid plant owners to make decision on evaluating design changes as well as resolving operational issues. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Columbia, Missouri: Using Energy Data to Reduce Emissions and Achieve Low-Income Household Energy Savings (City Energy: From Data to Decisions)

This fact sheet "Columbia, Missouri: Using Energy Data to Reduce Emissions and Achieve Low-Income Household Energy Savings" explains how the City of Columbia used data from the U.S. Department of Energy's Cities Leading through Energy Analysis and Planning (Cities-LEAP) and the State and Local Energy Data (SLED) programs to inform its city energy planning. It is one of ten fact sheets in the "City Energy: From Data to Decisions" series.

Cities leading through energy analysis and plannin↗

DARHT Multi-intelligence Seismic and Acoustic Data Analysis

The purpose of this report is to document the analysis of seismic and acoustic data collected at the Dual-Axis Radiographic Hydrodynamic Test (DARHT) facility at Los Alamos National Laboratory for robust, multi-intelligence decision making. The data utilized herein is obtained from two tri-axial seismic sensors and three acoustic sensors, resulting in a total of nine data channels. The goal of this analysis is to develop a generalized, automated framework to determine internal operations at DARHT using informative features extracted from measurements collected external of the facility. Our framework involves four components: (1) feature extraction, (2) data fusion, (3) classification, and finally (4) robustness analysis. Two approaches are taken for extracting features from the data. The first of these, generic feature extraction, involves extraction of statistical features from the nine data channels. The second approach, event detection, identifies specific events relevant to traffic entering and leaving the facility as well as explosive activities at DARHT and nearby explosive testing sites. Event detection is completed using a two stage method, first utilizing signatures in the frequency domain to identify outliers and second extracting short duration events of interest among these outliers by evaluating residuals of an autoregressive exogenous time series model. Features extracted from each data set are then fused to perform analysis with a multi-intelligence paradigm, where information from multiple data sets are combined to generate more information than available through analysis of each independently. The fused feature set is used to train a statistical classifier and predict the state of operations to inform a decision maker. We demonstrate this classification using both generic statistical features and event detection and provide a comparison of the two methods. Finally, the concept of decision robustness is presented through a preliminary analysis where uncertainty is added to the system through noise in the measurements.

58 GEOSCIENCES↗

Second Target Station Target Segment PIE Dose Estimates

The purpose of the analysis described in this document is to support post irradiation examination (PIE) future decisions as related to the Second Target Station (STS) Project. The STS Project is discussing plans for future PIE studies, and the radionuclide inventories and dose rates included in this study will aid decision makers as they consider the need for various PIE-related machinery. Three configurations of the lasagna-type target segment are evaluated for this analysis: a single bare tungsten plate, a slice of the segment containing a single tungsten plate, and the upstream half of a segment. The lasagna type target segment used in this analysis has a precipitation hardenable nickel alloy (UNS N07718 or inconel 718) shroud, copper (C10100) cladding, and pure tungsten plates. The target segment is evaluated for 10 years of operation followed by two decay periods: 6 months and 2 years. For each decay period, decay gamma dose rates are tallied 30 cm from the upstream, downstream, top, bottom, and sides of the configurations. Radionuclide inventories are also provided in this report separated by material. The highest dose rates calculated in this study are 2872 rem/hr 30 cm away from the top and bottom of the half-target configuration after 6 months of decay.

36 MATERIALS SCIENCE↗

Application of Risk Assessment Tools in the Continuous Risk Management (CRM) Process

Marshall Space Flight Center (MSFC) of the National Aeronautics and Space Administration (NASA) is currently implementing the Continuous Risk Management (CRM) Program developed by the Carnegie Mellon University and recommended by NASA as the Risk Management (RM) implementation approach. The four most frequently used risk assessment tools in the center are: (a) Failure Modes and Effects Analysis (FMEA), Hazard Analysis (HA), Fault Tree Analysis (FTA), and Probabilistic Risk Analysis (PRA). There are some guidelines for selecting the type of risk assessment tools during the project formulation phase of a project, but there is not enough guidance as to how to apply these tools in the Continuous Risk Management process (CRM). But the ways the safety and risk assessment tools are used make a significant difference in the effectiveness in the risk management function. Decisions regarding, what events are to be included in the analysis, to what level of details should the analysis be continued, make significant difference in the effectiveness of risk management program. Tools of risk analysis also depends on the phase of a project e.g. at the initial phase of a project, when not much data are available on hardware, standard FMEA cannot be applied; instead a functional FMEA may be appropriate. This study attempted to provide some directives to alleviate the difficulty in applying FTA, PRA, and FMEA in the CRM process. Hazard Analysis was not included in the scope of the study due to the short duration of the summer research project.

Paul S. Ray↗

SPECs Early-Stage Decision Model: User Manual

The SPECs Early-Stage Decision (ESD) model is central to the SPECs procurement solutions toolkit. The ESD model is an Excel-based spreadsheet model, which provides information about the economic and strategic value of a proposed battery-storage project or solar-plus-storage (solar-plus) project. The model can be used to explore combinations of storage-related project value streams in order to define a potential project, while educating co-op decision-makers about project benefits and costs. A sensitivity analysis function speeds the development of "what-if" scenarios. A gap analysis function solves for top-priority metrics and supports the inclusion of hard-to-monetize strategic values, such as the value of storage to defer costly system upgrades in light of increasing distributed solar and other distributed energy resources (DERs). Model outputs include the utility data, assumptions, and use-case scenarios that are recommended content for the requests for proposals (RFPs). The model may also provide an initial "sanity check" for RFP responses, supporting further discussions among utility staff, vendors and stakeholders. The ESD is not a "finance-grade" modeling tool, and users are cautioned to be mindful of its limitations, but the model has been reviewed by users, who recommend it as a way to drive faster and better project design and planning, as well as to facilitate better communications with vendors, grid partners, and stakeholders.

14 SOLAR ENERGY↗

Single-shot Quantum Signal Processing Interferometry

Quantum systems of infinite dimension, such as bosonic oscillators, provide vast resources for quantum sensing. Yet, a general theory on how to manipulate such bosonic modes for sensing beyond parameter estimation is unknown. We present a general algorithmic framework, quantum signal processing interferometry (QSPI), for quantum sensing at the fundamental limits of quantum mechanics by generalizing Ramsey-type interferometry. Our QSPI sensing protocol relies on performing nonlinear polynomial transformations on the oscillator's quadrature operators by generalizing quantum signal processing (QSP) from qubits to hybrid qubit-oscillator systems. We use our QSPI sensing framework to make efficient binary decisions on a displacement channel in the single-shot limit. Theoretical analysis suggests the sensing accuracy, given a single-shot qubit measurement, scales inversely with the sensing time or circuit depth of the algorithm. We further concatenate a series of such binary decisions to perform parameter estimation in a bit-by-bit fashion. Numerical simulations are performed to support these statements. Our QSPI protocol offers a unified framework for quantum sensing using continuous-variable bosonic systems beyond parameter estimation and establishes a promising avenue toward efficient and scalable quantum control and quantum sensing schemes beyond the NISQ era.

Physics↗

Transition Analysis of Promising U.S. Future Fuel Cycles Using ORION

The US Department of Energy Office of Fuel Cycle Technologies performed an evaluation and screening (E&S) study of nuclear fuel cycle options to help prioritize future research and development decisions. Previous work for this E&S study focused on establishing equilibrium conditions for analysis examples of 40 nuclear fuel cycle evaluation groups (EGs) and evaluating their performance according to a set of 22 standardized metrics. Following the E&S study, additional studies are being conducted to assess transitioning from the current US fuel cycle to future fuel cycle options identified by the E&S study as being most promising. These studies help inform decisions on how to effectively achieve full transition, estimate the length of time needed to undergo transition from the current fuel cycle, and evaluate performance of nuclear systems and facilities in place during the transition. These studies also help identify any barriers to achieve transition. Oak Ridge National Laboratory (ORNL) Fuel Cycle Options Campaign team used ORION to analyze the transition pathway from the existing US nuclear fuel cycle—the once-through use of low-enriched-uranium (LEU) fuel in thermal-spectrum light water reactors (LWRs) —to a new fuel cycle with continuous recycling of plutonium and uranium in sodium fast reactors (SFRs). This paper discusses the analysis of the transition from an LWR to an SFR fleet using ORION, highlights the role of lifetime extensions of existing LWRs to aid transition, and discusses how a slight delay in SFR deployment can actually reduce the time to achieve an equilibrium fuel cycle.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Earth Independent Medical Operations (EIMO) Datascope: Challenges and Potential Solutions

Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.

J Lemery↗

Earth Independent Medical Operations (EIMO) Datascope: Challenges and Potential Solutions

Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.

Medical Operations↗

Sub-pilot-scale Production of High-Value Products from U.S. Coals

Investigators from the University of Utah, University of Wyoming and Marshall University pursued a program to study the conversion of raw coal to high-value products of carbon fiber and silicon carbide. Team members also developed an initial framework for a data portal that can incorporate laboratory data on coal processing and product quality, and also work with tools for machine learning for data analysis, data visualization and economic assessment. Experimental R&D efforts focused on the conversion of raw coal to coal tar and other byproducts, and the resulting tar intermediates were upgraded to form anisotropic and isotropic pitch materials. These pitch materials were produced from coal using both thermal (pyrolysis) and chemical (mild solvolysis liquefaction) decomposition of raw coal. Four different coals were studied: Utah bituminous coal (Sufco), Wyoming PRB coal (Black Thunder), Illinois bituminous coal (Illinois #6), and West Virginia bituminous coal (Flying Eagle). Both metallurgical-grade coking coals and lower-grade steam coals were investigated, and controlled secondary gas-phase reactions were used during a two-stage pyrolysis process to induce cracking and condensation reactions among the pyrolytic tar species. This approach successfully improved the performance of the lower grade coals for yielding pitch materials, with properties more consistent with a commercial-grade pitch that had previously demonstrated success for quality carbon fiber production. The use of waste plastic materials was also studied, to help improve physical and chemical characteristics of the intermediate tars and final pitch product; in particular, for lowering the pitch softening point to an acceptable level for melt spinning carbon fiber. Mild solvolysis liquefaction was also used as a method for producing pitch for carbon fiber production. As expected, significantly higher pitch yields were obtained using this approach, and waste plastic materials were also successfully used to reduce pitch softening point to an acceptable level. The plastic materials were also utilized to create a solvent for the mild solvolysis process, and this plastic-derived solvent was shown to provide results consistent with more expensive commercial chemical solvents, and could thus avoid the need for costly recovery and recycle of a liquefaction solvent. Additional experimental R&D focused on the production of silicon carbide (β-SiC) from the residual char byproduct from pitch production, and also on the production of carbon fiber from the anisotropic pitch. SiC was successfully synthesized using a mixture of residual char and sandstone at a ratio of 1:1. Reaction temperature and residence time were optimized and yielded a product purity of 81%. For carbon fiber production, the most successful pitch samples were obtained from the mild solvolysis liquefaction approach, combined with the use of a plastic (HDPE)-derived solvent. Fiber properties improved over time as laboratory fiber production methodologies improved, and final yields of carbon fiber were obtained with a diameter of 12.14 ± 1.10 um, Modulus of 173.73 ± 15.25 GPa, and Tensile Strength of 1.04 ± 0.10 GPa. A proof-of-concept Modern Community Research Data Portal (MCRDP) was developed and deployed for coal and coal-derived pitch characterization, with the full support of (i) remote web-based access, (ii) distributed analysis, (iii) interactive visualization and exploration, (iv) shared and long-term data access, (v) advanced query capabilities and (vi) real-time collaboration. The Coal to Products Data Portal “coaltoproducts.org” provides researchers with space to store and share data within a project, tools for analyzing and understanding data for scientific investigation, and the ability to publish data to the broader community for reproducibility. The portal leverages the Material Commons 2.0 (MC) platform developed by the Center for PRedictive Integrated Structural Materials Science (PRISMS) of the University of Michigan, to achieve long-term longevity of data collections and, more importantly, collaborative science. A number of data visualization tools were also assessed and implemented for interrogating the experimental and modeling data. The machine learning portion of this project analyzed datasets from two different coal conversion processes performed on a diverse set of coal samples from both the coal pyrolysis experiments and the solvent liquefaction experiments. The work was initiated by exploring standard regression models on the pyrolysis data, aiming to understand the impact of sample characteristics and processing conditions on key product metrics. Over the course of the project, the focus expanded to include a variety of machine learning tools, delving into both supervised and unsupervised learning methods. Models tested on the pyrolysis data included linear, ridge, lasso, elastic-net, Gaussian process, random forest regression, and AutoSklearn, and the approach was continually refined to enhance predictive accuracy and model interpretability. Similar techniques were applied to the liquefaction data with an additional focus on feature engineering. Along with mesophase content, additional outputs of interest were the pitch yield, softening point, and QI content. Insights derived from these analyses are crucial in determining the factors influencing the quality and yield of coal-derived products. As the work progressed, the research evolved from foundational model comparisons to analyses of random forests, decision paths, and feature importance scores. A thorough market analysis was performed to examine the prospects of coal-based carbon fibers. The best opportunities for coal come from its lower and more stable price relative to petroleum, particularly for subbituminous coals, which is the primary advantage that a coal refinery may have over a petroleum refinery. Before a commercial CTP production facility can be modeled, however, several things need to be understood regarding the nature of the would-be coal refinery. These include the technology to be deployed, the size of facility, the volume(s) of co-product(s), and the waste and emissions profile of the plant. The volume of co-products and waste may be substantial and will require separate market analysis to ensure viability. In the near-term, the importance of coal tar pitch, in the form of carbon pitch, to the aluminum and steel industries is likely to overshadow the alternative use of this material as an input for carbon fiber. The importance of steel and aluminum in building materials, and the need for carbon materials in their manufacturing, will ensure that demand for these products remains for the long run. In addition, carbon fiber may also be the best substitute for steel and aluminum well into the future. While society will eventually be able to shift production of much of its electricity needs to renewables, it will not be able to shift away from fossil fuels for production of high-strength construction and vehicular materials. Demand for carbon fiber is expected to increase quickly, but the volume of carbon fiber and the amount of coal that would be needed to produce even a sizeable share of this market may still be relatively small compared to current coal production. Thus, other coal-based products like graphene, graphite, carbon foams, resins, and carbon-based building products will play important roles in sustaining coal production as coal-fired power generation continues to decline.

01 COAL, LIGNITE, AND PEAT↗

Automated Translation and Thermal Zoning of Digital Building Models for Energy Analysis

Building energy simulation is valuable during the early stages of design, when decisions can have the greatest impact on energy performance. However, preparing digital design models for building energy simulation typically requires tedious manual alteration. This paper describes a series of five automated steps to translate geometric data from an unzoned CAD model into a multi-zone building energy model. First, CAD input is interpreted as geometric surfaces with materials. Second, surface pairs defining walls of various thicknesses are identified. Third, normal directions of unpaired surfaces are determined. Fourth, space boundaries are defined. Fifth, optionally, settings from previous simulations are applied, and spaces are aggregated into a smaller number of thermal zones. Building energy models created quickly using this method can offer guidance throughout the design process.

97 MATHEMATICS AND COMPUTING↗

System design of the Pioneer Venus spacecraft. Volume 3: Systems analysis

The mission, systems, operations, ground systems, and reliability analysis of the Thor/Delta baseline design used for the Pioneer Space Probe are discussed. Tradeoff decisions concerning spin axis orientation, bus antenna design, communication system design, probe descent, and reduced science payload are analyzed. The reliability analysis is made for the probe bus mission, large probe mission, and small probe mission. Detailed mission sequences were established to identify critical areas and provide phasing of critical operation.

Fisher, J. N.↗

Are We Appropriately Selecting Therapy For Patients With Cervical Cancer? Longitudinal Patterns-of-Care Analysis for Stage IB-IIB Cervical Cancer

Purpose: We performed a patterns-of-care analysis evaluating the effects of newer technology and recent research findings on treatment decisions over 26 years to determine whether patients with cervical cancer are being appropriately selected for treatment to optimize the therapeutic ratio. Methods and Materials: A retrospective analysis was conducted using the Surveillance, Epidemiology and End Results (SEER) program from 1983 to 2009. We identified 10,933 women with stage IB-IIB cervical carcinoma. Results: Of the 10,933 subjects identified, 40.1% received surgery, 26.8% received radiation (RT), and 33.1% received surgery plus RT. RT use increased after 2000 compared to prior to 2000, with a corresponding decrease in surgery and surgery plus RT. Among patients with risk factors including tumor size >4 cm, positive parametria, and positive lymph nodes, declining use of surgery plus RT was observed. However, 23% of patients with tumors >4 cm, 20% of patients with positive parametria, and 55% of node-positive patients continued to receive surgery plus RT as of 2009. Factors associated with increased use of surgery plus RT included patient age <50 and node-positive status. Conclusions: In this largest patterns-of-care analysis to date for patients with locally advanced cervical cancer, we found a substantial proportion of patients continue to undergo surgery followed by radiation, despite randomized data supporting the use of definitive radiation therapy, with lower morbidity than surgery and radiation.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

Comparative Analysis for the Chicago Energy Retrofit Project: Project Report

For the Chicago Loop Energy Efficiency Retrofit project, Argonne is developing and piloting analytic methods that capture the energy performance of individual commercial buildings; can be scaled to communities; and appropriately evaluate retrofit options in real contexts. The decision tools under development span and integrate building energy performance calculations, uncertainty analysis, and financial risk assessment. A key outcome from this project is an energy calculator, EECalc, developed in collaboration with the Georgia Institute of Technology. The energy modeling methodology used in EECalc was chosen specifically because it (1) is computationally efficient, (2) uses only observable building data that take a relatively low level of effort to compile, and (3) requires minimal modeling expertise and judgment from the user. These features expand the possibilities for EECalc’s widespread, large-scale use in retrofit and uncertainty analysis.

Guzowski, Leah B.↗

Sampling and Analysis Plan (SAP) for Assessment of LANL-Derived Residual Radionuclides in Soils within Revised Tract A-16-a for Land Transfer Decisions

Material Disposal Area (MDA) B is an inactive subsurface disposal site located south of DP Road in Technical Area 21 (TA 21). It is approximately 1600 ft east of the intersection of DP Road and Trinity Drive (Figure 1). Land proposed for for transfer is identified as tract A-16-a and constitutes approximately 30 acres with about 6 of those acres being above the trench from which waste was removed (LANL 2012a).

61 RADIATION PROTECTION AND DOSIMETRY↗